Field assessment of active ingredient quantity in pharmaceutical tablets with limited calibration of near infrared spectra: An application to ciprofloxacin tablets.

Portable near-infrared (NIR) spectrophotometers have emerged as valuable tools for identifying substandard and falsified pharmaceuticals (SFPs). Integration of these devices with chemometric and machine learning models enhances their ability to provide quantitative chemical insights. However, different NIR spectrophotometer models vary in resolution, sensitivity, and responses to environmental factors such as temperature and humidity, necessitating instrument-specific libraries that hinder the wider adoption of NIR technology. This study addresses these challenges and seeks to establish a robust approach to promote the use of NIR technology in post-market pharmaceutical analysis. We developed support vector machine and partial least squares regression models based on binary mixtures of lab-made ciprofloxacin and microcrystalline cellulose, then applied the models to ciprofloxacin dosage forms that were assayed with high performance liquid chromatography (HPLC). A receiver operating characteristic (ROC) analysis was performed to set spectrophotometer independent NIR metrics to evaluate ciprofloxacin dosage forms as "meets standard," "needs HPLC assay," or "fails standard." Over 200 ciprofloxacin tablets representing 50 different brands were evaluated using spectra acquired from three types of NIR spectrophotometer with 85% of the prediction agreeing with HPLC testing. This study shows that non-brand-specific predictive models can be applied across multiple spectrophotometers for rapid screening of the conformity of pharmaceutical active ingredients to regulatory standard.

Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy.

Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules.

Discrimination of Substandard and Falsified Formulations from Genuine Pharmaceuticals Using NIR Spectra and Machine Learning.

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

High-Affinity Points of Interaction on Antibody Allow Synthesis of Stable and Highly Functional Antibody-Gold Nanoparticle Conjugates.

Many emerging nanobiotechnologies rely on the proper function of proteins immobilized on gold nanoparticles. Often, the surface chemistry of the AuNP is engineered to control the orientation, surface coverage, and structure of the adsorbed protein to maximize conjugate function. Here, we chemically modified antibody to investigate the effect of protein surface chemistries on adsorption to AuNPs. A monoclonal anti-horseradish peroxidase IgG antibody (anti-HRP) was reacted with N-succinimidyl acrylate (NSA) or reduced dithiobissuccinimidyl propionate (DSP) to modify lysine residues. Zeta potential measurements confirmed that both chemical modifications reduced the localized regions of positive charge on the protein surface, while the DSP modification incorporated additional free thiols. Dynamic light scattering confirmed that native and chemically modified antibodies adsorbed onto AuNPs to form bioconjugates; however, adsorption kinetics revealed that the NSA-modified antibody required significantly more time to allow for the formation of a hard corona. Moreover, conjugates formed with the NSA-modified antibody lost antigen-binding function, whereas unmodified and DSP-modified antibodies adsorbed onto AuNPs to form functional conjugates. These results indicate that high-affinity functional groups are required to prevent protein unfolding and loss of function when adsorbed on the AuNP surface. The reduced protein charge and high-affinity thiol groups on the DSP-modified antibody enabled pH-dependent control of protein orientation and the formation of highly active conjugates at solution pHs (<7.5) that are inaccessible with unmodified antibody due to conjugate aggregation. This study establishes parameters for protein modification to facilitate the formation of highly functional and stable protein-AuNP conjugates.

Probing the Mechanism of Antibody-Triggered Aggregation of Gold Nanoparticles.

The unique physicochemical properties of gold nanoparticles (AuNPs) provide many opportunities to develop novel biomedical technologies. The surface chemistry of AuNPs can be engineered to perform a variety of functions, including targeted binding, cellular uptake, or stealthlike properties through the immobilization of biomolecules, such as proteins. It is well established that proteins can spontaneously adsorb onto AuNPs, to form a stable and functional bioconjugate; however, the protein-AuNP interaction may result in the formation of less desirable protein-AuNP aggregates. Therefore, it is imperative to investigate the protein-AuNP interaction and elucidate the mechanism by which protein triggers AuNP aggregation. Herein, we systematically investigated the interaction of immunoglobulin G (IgG) antibody with citrate-capped AuNPs as a function of solution pH. We found that the addition of antibody triggers the aggregation of AuNPs for pH < 7.5, whereas a monolayer of antibody adsorbs onto the AuNP to form a stable bioconjugate when the antibody is added to AuNPs at pH ≥ 7.5. Our data identifies electrostatic bridging between the antibody and the negatively charged AuNPs as the mechanism by which aggregation occurs and rules out protein unfolding and surface charge depletion as potential causes. Furthermore, we found that the electrostatic bridging of AuNPs is reversible within the first few hours of interaction, but the protein-AuNP interactions strengthen over 24 h, after which the protein-AuNP aggregate is irreversibly formed. From this data, we developed a straightforward approach to acrylate the basic residues on the antibody to prevent protein-induced aggregation of AuNP over a wide pH range. The results of this study provide additional insight into antibody-nanoparticle interactions and provide a pathway to control the interaction with the potential to enhance the conjugate function.

Role of Free Thiol on Protein Adsorption to Gold Nanoparticles.

Protein-gold nanoparticle (AuNP) bioconjugates have many potential applications in nanomedicine. A thorough understanding of the interaction between the protein and the AuNP is critical to engineering these functional bioconjugates with desirable properties. In this work, we investigate the role of free thiols presented by the protein on the stability of the protein-AuNP conjugate. Human serum albumin (HSA) was modified with 2-iminothiolane (Traut's reagent) to introduce additional thiols onto the protein surface, and three variants of HSA were synthesized to present 1, 5, and 20 free thiols by controlling the molar excess of the chemical modifier. Protein exchange studies on AuNPs were conducted using these HSA species and an IgG antibody which exhibited 10 free thiols. Antibody-AuNP conjugates were synthesized, purified, and dispersed in solutions containing each of the HSA species. No protein exchange was detected with the HSA or modified HSA containing 5 thiols; however, 85% of the antibody was displaced on the AuNP surface by the extensively thiolated HSA presenting 20 free thiols. Furthermore, the impact of the protein adsorption sequence was probed in which each of the HSA species were preadsorbed onto the AuNP and dispersed in a solution of antibody. The antibody fully displaced the HSA with a single thiol from the AuNP within 3 h, required 24 h to completely displace the modified HSA containing 5 thiols, and was unable to displace the modified HSA containing 20 thiols. These results indicate that the number of Au-S interactions governs the binding interaction between the protein and the AuNP. This work provides further insight into the protein-AuNP binding mechanism and identifies important design principles for engineered proteins to optimize bioconjugates.

Antibodies Irreversibly Adsorb to Gold Nanoparticles and Resist Displacement by Common Blood Proteins.

Gold nanoparticles (AuNPs) functionalized with proteins to impart desirable surface properties have been developed for many nanobiotechnology applications. A strong interaction between the protein and nanoparticle is critical to the formation of a stable conjugate to realize the potential of these emerging technologies. In this work, we examine the robustness of a protein layer adsorbed onto gold nanoparticles while under the stress of a physiological environment that could potentially lead to protein exchange on the nanoparticle surface. The adsorption interaction of common blood plasma proteins (transferrin, human serum albumin, and fibrinogen) and anti-horseradish peroxidase antibody onto AuNPs is investigated by nanoparticle tracking analysis. Our data show that a monolayer of protein is formed at saturation for each protein, and the maximum size increase for the conjugate, relative to the AuNP core, correlates with the protein size. The binding affinity of each protein to the AuNP is extracted from a best fit of the adsorption isotherm to the Hill equation. The antibody displays the greatest affinity (Kd = 15.2 ± 0.8 nM) that is ∼20-65 times stronger than the affinity of the other plasma proteins. Antibody-AuNP conjugates were prepared, purified, and suspended in solutions of blood plasma proteins to evaluate the stability of the antibody layer. An enzyme-mediated assay confirms that the antibody-AuNP interaction is irreversible, and the adsorbed antibody resists displacement by the plasma proteins. This work provides insight into the capabilities and potential limitations of antibody-AuNP-enabled technologies in biological systems.